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1
artículo
Publicado 2022
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“The most devastating disease to humanity is commonly known as elephantiasis. Infection usually acquired in childhood but visible indication like pain, disfiguring occur later in life. Severely affected people will have a permanent disability. Not only had it generated physical challenges it also cost for social, psychological, and economical losses. The impact of the disease is so painful and devastating among young men and women as they live with the lifelong disfiguring condition. As the parasite attacks directly to the lymphatic system of the body whose primary function is to drain all the harmful components and impurities from tissues and cells also make a strong immune system of the body to fight against infection and diseases. Since the parasite attack damages the lymphatic vessels and capillaries. Hence the effect is on the flow of lymph resulting in lymphoedema. 90% of the cas...
2
artículo
Publicado 2022
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“The most devastating disease to humanity is commonly known as elephantiasis. Infection usually acquired in childhood but visible indication like pain, disfiguring occur later in life. Severely affected people will have a permanent disability. Not only had it generated physical challenges it also cost for social, psychological, and economical losses. The impact of the disease is so painful and devastating among young men and women as they live with the lifelong disfiguring condition. As the parasite attacks directly to the lymphatic system of the body whose primary function is to drain all the harmful components and impurities from tissues and cells also make a strong immune system of the body to fight against infection and diseases. Since the parasite attack damages the lymphatic vessels and capillaries. Hence the effect is on the flow of lymph resulting in lymphoedema. 90% of the cas...
3
artículo
Publicado 2022
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In this research satellite image classification for environmental change prediction using image processing and machine learning methods is used. As we know satellite images is one of the important sources of collecting information for all area and region of interest which is suitable for any difficult situation around the world. The satellite image helps in collecting information on areas which is unpredictable and unreachable through digital cameras. In this research work, an advanced study on environmental change perdition has been examined using three classes’ ice land area, cropland area, and forest area. This research help in characterizing the type of satellite image classification for the particular three classes. The following stages have been considered are preprocessing, segmentation, and classification methods using K- Nearest Neighbor classifier. The present investigation r...
4
tesis de grado
Publicado 2024
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Las tecnologías emergentes posibilitan reenfocar las estrategias de comunicación e interacción con los clientes y usuarios con innovaciones importantes, tales como el uso de vídeos de 360 y la realidad virtual inmersiva (VR) en la promoción turística y hotelera. El objetivo de este trabajo es aprovechar estas tecnologías para optimizar la creación de experiencias en 360, a partir de una automatización de procesos enfocada en la clasificación de imágenes que compondrán dicha experiencia. En nuestra propuesta diseñamos una red neuronal convolucional (CNN), cuyas funciones esenciales son el proceso de extracción de características y el proceso de clasificación y salida de imágenes, debido a que serán utilizados para la composición de tours virtuales. La etapa de extracción de características, está compuesta por varias capas ocultas, como la capa de convolución, la fun...
5
artículo
Publicado 2020
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Palm swamps are ecosystems with a predominant presence of the palm Mauritia flexuosa, which provides important socio-economic and environmental benefits to the inhabitants of the Peruvian Amazon. The objective of this study is to determine the extent of palm swamp forest in the Ucayali region, Peru, through the supervised classification method of satellite images generated by the LANDSAT 8 Earth Observation satellite through the OLI-TIRS sensor, corresponding to Ucayali region in 2017. We visually interpreted the combination of bands 5 (Near Infrared (NIR), with wavelengths of 0.85 - 0.88 μm), 6 (Short Wave Infrared 1 (SWIR 1), with wavelengths of 1.57 - 1.65 μm) and 2 (Blue, with wavelengths of 0.45 - 0.51 μm) to obtain the map of palm swamp forest with an interpretation scale of 1:100 000 and a minimum mapping area of 5.00 ha. We determined a coverage of 65 120.04 ha of palm swamp f...
6
artículo
Publicado 2018
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Pneumonia is one of the major causes of child mortality, yet with a timely diagnosis, it is usually curable with antibiotic therapy. In many developing regions, diagnosing pneumonia remains a challenge, due to shortages of medical resources. Lung ultrasound has proved to be a useful tool to detect lung consolidation as evidence of pneumonia. However, diagnosis of pneumonia by ultrasound has limitations: it is operator-dependent, and it needs to be carried out and interpreted by trained personnel. Pattern recognition and image analysis is a potential tool to enable automatic diagnosis of pneumonia consolidation without requiring an expert analyst. This paper presents a method for automatic classification of pneumonia using ultrasound imaging of the lungs and pattern recognition.
7
artículo
Publicado 2025
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The emergence of Machine Learning (ML) technologies and their integration into agriculture has demonstrated a significant impact on disease detection in crops, enabling continuous monitoring and enhancing risk planning and management. This study applied image processing techniques such as thresholding, gamma correction, and the Stretched Neighborhood Effect Color to Grayscale (SNECG) method, alongside ML, to develop a predictive model for identifying five types of rice diseases. The ML techniques used included Logistic Regression, Multilayer Perceptron, Support Vector Machines, Decision Trees, and Random Forests (RF). Hyperparameters were optimized and evaluated through 5-fold cross-validation. In the results, the SNECG method successfully converted images to grayscale, capturing essential features of lesions on rice leaves. The ML models developed with these techniques showed evaluation...
8
artículo
Publicado 2022
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The following study proposes to analyze and compare the computational response provided by the Single Board Computers (SBC) Raspberry Pi CM4 and Nvidia Jetson Nano because those are important elements in machine learning applications and implementation of automated systems. Both were chosen due to their similar specifications to achieve a fair comparison. For the development of this research, an algorithm was implemented with a Support Vector Machine to be able to compare the performance in real-time of both computers based on performance metrics such as execution time, algorithm accuracy, CPU performance, and temperature. To validate results, there is a database of 2186 white asparagus images, which were classified based on attributes such as length, curvature, diameter, and purple hue. These attributes are established by the Peruvian Asparagus and Vegetable Institute (IPEH) in the Peru...
9
artículo
Publicado 2024
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This project focuses on developing an NLP-based text analysis tool to evaluate Android app user feedback, specifically collected from F-Droid. The lack of an automated solution to analyze and understand these opinions, classifying them into specific topics, motivates research. The goal is to provide developers, users, and data analysts with a detailed view of user preferences and perceptions. Using data sets in English between 2014 and 2017, the proposal is implemented in Python with the Pandas library. The BERT model is used for classification, with a specific focus on the comparison of different models. The graphical interface is built in Visual Studio, allowing users to enter comments and obtain topic rankings, along with word cloud visualizations.
10
objeto de conferencia
The presence of outliers, noise, corrupt pieces of data and great quantity of samples in a multispectral image, makes the segmentation analysis work tedious. The fuzzy clustering approach, specially, is susceptible to inhomogeneity of characteristics. Furthermore, many algorithms such us FCM, PFCM, FCC, FWCM and modification aim to solve these problems by integrating spacial information. This process is carried through the analysis of the sample's neighborhood. This paper proposes the integration of the sample presence probability into a ”term” like form inside the existent model NFCC. This algorithm presents the basic steps for fuzzy clustering. With a middle variant that integrates the measure between each sample to all the centroids, this replaces the existent term by a new term. This new term integrates the spatial relationship between each sample of the multispectral image into ...
11
artículo
Publicado 2024
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This article introduces an innovative mobile solution for Pterygium detection, an eye disease, using a classification model based on the convolutional neural network (CNN) architecture ResNext50 in images of the anterior segment of the eye. Four models (ResNext50, ResNet50, MobileNet v2, and DenseNet201) were used for the analysis, with ResNext50 standing out for its high accuracy and diagnostic efficiency. The research, focused on applications for ophthalmological medical centers in Lima, Peru, explains the process of development and integration of the ResNext50 model into a mobile application. The results indicate the high effectiveness of the system, highlighting its high precision, recall, and specificity, which exceed 85%, thus showing its potential as an advanced diagnostic tool in ophthalmology. This system represents a significant tool in ophthalmology, especially for areas with ...
12
artículo
Publicado 2020
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Over the last years, Convolutional Neural Networks have been extensively used for solving problems such as image classification, object segmentation, and object detection. However, deep neural networks require a great deal of data correctly labeled in order to perform properly. Generally, generation and labeling processes are carried out by recruiting people to label the data manually. To overcome this problem, many researchers have studied the use of data generated automatically by a renderer. To the best of our knowledge, most of this research was conducted for general-purpose domains but not for specific ones. This paper presents a methodology to generate synthetic data and train a deep learning model for the segmentation of pieces of machinery. For doing so, we built a computer graphics synthetic 3D scenery with the 3D models of real pieces of machinery for rendering and capturing vi...
13
artículo
Publicado 2023
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The introduction of artificial intelligence methods and techniques in the construction industry has fostered innovation and constant improvement in the automation of monitoring and control processes at construction sites, although there are areas where more studies still need to be conducted. This paper proposes a method to determine the criticality of cracks in concrete samples. The proposed method uses a previously trained YOLOv4 neural network to identify concrete cracks. Then, the region of interest, determined by the bounding box resulting from the neural network model classification, is extracted. Finally, the extracted image is converted to negative grayscale to quantify the number of white pixels above a certain threshold, automatically allowing the system to characterize the fracture’s extent and criticality. The classification module reached a veracity between 98.36% and 99.7...
14
artículo
Publicado 2021
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We want to thank the Image Processing Research Laboratory. (INTI-Lab) and the Universidad de Ciencias y Humanidades. (UCH) for their support in this research, the National Fund for. Scientific, Technological and Technological Innovation (FONDECYT), according to the research: ?SAMAYCOV: ?Desarrollo de un dispositivo electr?nico port?til a bajo costo para evaluar riesgo de neumon?a basado en sonido pulmonar anormal en pacientes con sospecha de COVID-19 en zonas vulnerables?. CONVENIO 054-2020-FONDECYT?; for the financing of this research and the Electronics Laboratory of the UCH for assigning us their facilities and being able to carry out the respective tests.
15
artículo
Publicado 2024
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The application of computer technologies associated with sensors and artificial intelligence (AI) in the quantification and qualification of quality parameters of meat products of various domestic species is an area of research, development, and innovation of great relevance in the agri-food industry. This review covers the most recent advances in this area, highlighting the importance of computer vision, artificial intelligence, and ultrasonography in evaluating quality and efficiency in meat products’ production and monitoring processes. Various techniques and methodologies used to evaluate quality parameters such as colour, water holding capacity (WHC), pH, moisture, texture, and intramuscular fat, among others related to animal origin, breed and handling, are discussed. In addition, the benefits and practical applications of the technology in the meat industry are examined, such as...
16
artículo
Publicado 2024
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The application of computer technologies associated with sensors and artificial intelligence (AI) in the quantification and qualification of quality parameters of meat products of various domestic species is an area of research, development, and innovation of great relevance in the agri-food industry. This review covers the most recent advances in this area, highlighting the importance of computer vision, artificial intelligence, and ultrasonography in evaluating quality and efficiency in meat products’ production and monitoring processes. Various techniques and methodologies used to evaluate quality parameters such as colour, water holding capacity (WHC), pH, moisture, texture, and intramuscular fat, among others related to animal origin, breed and handling, are discussed. In addition, the benefits and practical applications of the technology in the meat industry are examined, such as...
17
tesis de grado
Publicado 2025
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Enlace
Este proyecto se orientó al diseño e implementación de un sistema automatizado destinado a clasificar y recolectar granos de quinua en condiciones de laboratorio. Para ello se tomaron como criterios de diferenciación tanto el color (blanco, rojo y negro) como el tamaño (pequeño, mediano y grande). La propuesta surgió como alternativa al procedimiento manual tradicional, el cual suele generar resultados poco confiables debido a su carácter subjetivo, la lentitud del proceso y las limitaciones en precisión. El sistema estuvo compuesto por un dispensador que liberó los granos de manera individual hacia una zona de trabajo con iluminación controlada, donde una cámara ubicada en la parte superior capturó las imágenes para su análisis. A través de un modelo de visión artificial se determinaron las características físicas de cada grano, y posteriormente un soplador lateral, e...
18
artículo
The dentigerous cyst, included in the classification of odontogenic cysts by the WHO in 2017, constitutes a relevant pathology in the bucco-maxillofacial area. Although its growth is slow and asymptomatic, when it reaches large dimensions, it has the capacity to cause bone destruction in jaws, which causes aesthetic and functional alterations. In addition, it has a high prevalence, with the odontogenic cyst as the most common, after the inflammatory radicular cyst. Radiology represents a fundamental tool in its diagnosis and many times its detection constitutes a radiographic finding. Normally, 2D techniques such as periapical, panoramic and occlusal radiography are used, which play a very important role in its detection and diagnosis. However, it must be considered that the similarity of the dentigerous cyst with other pathological processes, both clinically and radiographically, often ...
19
artículo
Publicado 2020
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Non-destructive determination of blueberry compound using spectral detection method is still a challenge due to the spectral THZ variation caused by abundant biological variations, such as geographic origins and harvest seasons. In order to investigate the potential of Terahertz time-domain spectroscopy to classify fruit maturity states, terahertz spectra (0.5-10 THz) of 4 states of blueberry maturity were examined. The acquired data matrices were submitted to the application of MATLAB 2019b Classification Learner by using 24 classifier models. 84.3 is the highest accuracy, obtained by the Fine Gaussian SVM Algorithm Model with a 0.35 Kernel Scale and a Multiclass Method One vs One. The coefficients for this application of PCA are PC1 (79.9%) and PC2 (20.1%). It was concluded that the combined processing and classification of images obtained from Terahertz time-domain spectroscopy and us...
20
objeto de conferencia
Publicado 2020
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El texto completo de este trabajo no está disponible en el Repositorio Académico UPN por restricciones de la casa editorial donde ha sido publicado.
